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recursive partitioning

*The author of this computation has been verified*

R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)

Title produced by software: Recursive Partitioning (Regression Trees)

Date of computation: Fri, 24 Dec 2010 13:42:21 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t12931980458le5utnezz9mmxf.htm/, Retrieved Fri, 24 Dec 2010 14:40:46 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t12931980458le5utnezz9mmxf.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

4 1 27 5 26 49 35 4 1 36 4 25 45 34 5 1 25 4 17 54 13 2 1 27 3 37 36 35 3 2 25 3 35 36 28 5 2 44 3 15 53 32 4 1 50 4 27 46 35 4 1 41 4 36 42 36 4 1 48 5 25 41 27 4 2 43 4 30 45 29 5 2 47 2 27 47 27 4 2 41 3 33 42 28 3 1 44 2 29 45 29 4 2 47 5 30 40 28 3 2 40 3 25 45 30 3 2 46 3 23 40 25 4 1 28 3 26 42 15 3 1 56 3 24 45 33 4 2 49 4 35 47 31 2 2 25 4 39 31 37 4 2 41 4 23 46 37 3 2 26 3 32 34 34 4 1 50 5 29 43 32 4 1 47 4 26 45 21 3 1 52 2 21 42 25 3 2 37 5 35 51 32 2 2 41 3 23 44 28 4 1 45 4 21 47 22 5 2 26 4 28 47 25 4 1 NA 3 30 41 26 2 1 52 4 21 44 34 5 1 46 2 29 51 34 4 1 58 3 28 46 36 3 1 54 5 19 47 36 4 1 29 3 26 46 26 2 2 50 3 33 38 26 3 1 43 2 34 50 34 3 2 30 3 33 48 33 3 2 47 2 40 36 31 5 1 45 3 24 51 33 NA 2 48 1 35 35 22 4 2 48 3 35 49 29 4 2 26 4 32 38 24 4 1 46 5 20 47 37 2 2 NA 3 35 36 32 4 2 50 3 35 47 23 3 1 25 4 21 46 29 4 1 47 2 33 43 35 1 2 47 2 40 53 20 2 1 41 3 22 55 28 2 2 45 2 35 39 26 4 2 41 4 20 55 36 3 2 45 5 28 41 26 4 2 40 3 46 33 3 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.3171
R-squared	0.1006
RMSE	0.9882

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4	3.46043165467626	0.539568345323741
2	4	3.46043165467626	0.539568345323741
3	5	3.46043165467626	1.53956834532374
4	2	2.72727272727273	-0.727272727272727
5	3	2.72727272727273	0.272727272727273
6	5	3.46043165467626	1.53956834532374
7	4	3.46043165467626	0.539568345323741
8	4	3.46043165467626	0.539568345323741
9	4	3.46043165467626	0.539568345323741
10	4	3.46043165467626	0.539568345323741
11	5	3.46043165467626	1.53956834532374
12	4	3.46043165467626	0.539568345323741
13	3	3.46043165467626	-0.460431654676259
14	4	2.72727272727273	1.27272727272727
15	3	3.46043165467626	-0.460431654676259
16	3	2.72727272727273	0.272727272727273
17	4	3.46043165467626	0.539568345323741
18	3	3.46043165467626	-0.460431654676259
19	4	3.46043165467626	0.539568345323741
20	2	2.72727272727273	-0.727272727272727
21	4	3.46043165467626	0.539568345323741
22	3	2.72727272727273	0.272727272727273
23	4	3.46043165467626	0.539568345323741
24	4	3.46043165467626	0.539568345323741
25	3	3.46043165467626	-0.460431654676259
26	3	3.46043165467626	-0.460431654676259
27	2	3.46043165467626	-1.46043165467626
28	4	3.46043165467626	0.539568345323741
29	5	3.46043165467626	1.53956834532374
30	4	3.46043165467626	0.539568345323741
31	2	3.46043165467626	-1.46043165467626
32	5	3.46043165467626	1.53956834532374
33	4	3.46043165467626	0.539568345323741
34	3	3.46043165467626	-0.460431654676259
35	4	3.46043165467626	0.539568345323741
36	2	2.72727272727273	-0.727272727272727
37	3	3.46043165467626	-0.460431654676259
38	3	3.46043165467626	-0.460431654676259
39	3	2.72727272727273	0.272727272727273
40	5	3.46043165467626	1.53956834532374
41	4	3.46043165467626	0.539568345323741
42	4	2.72727272727273	1.27272727272727
43	4	3.46043165467626	0.539568345323741
44	2	2.72727272727273	-0.727272727272727
45	4	3.46043165467626	0.539568345323741
46	3	3.46043165467626	-0.460431654676259
47	4	3.46043165467626	0.539568345323741
48	1	3.46043165467626	-2.46043165467626
49	2	3.46043165467626	-1.46043165467626
50	2	2.72727272727273	-0.727272727272727
51	4	3.46043165467626	0.539568345323741
52	3	3.46043165467626	-0.460431654676259
53	4	2.72727272727273	1.27272727272727
54	3	3.46043165467626	-0.460431654676259
55	3	3.46043165467626	-0.460431654676259
56	5	3.46043165467626	1.53956834532374
57	3	3.46043165467626	-0.460431654676259
58	2	2.72727272727273	-0.727272727272727
59	1	3.46043165467626	-2.46043165467626
60	2	3.46043165467626	-1.46043165467626
61	5	3.46043165467626	1.53956834532374
62	4	3.46043165467626	0.539568345323741
63	4	3.46043165467626	0.539568345323741
64	3	3.46043165467626	-0.460431654676259
65	4	3.46043165467626	0.539568345323741
66	4	3.46043165467626	0.539568345323741
67	2	2.72727272727273	-0.727272727272727
68	3	3.46043165467626	-0.460431654676259
69	4	2.72727272727273	1.27272727272727
70	3	3.46043165467626	-0.460431654676259
71	2	2.72727272727273	-0.727272727272727
72	4	2.72727272727273	1.27272727272727
73	4	3.46043165467626	0.539568345323741
74	3	2.72727272727273	0.272727272727273
75	5	3.46043165467626	1.53956834532374
76	1	2.72727272727273	-1.72727272727273
77	3	3.46043165467626	-0.460431654676259
78	3	3.46043165467626	-0.460431654676259
79	5	3.46043165467626	1.53956834532374
80	2	3.46043165467626	-1.46043165467626
81	3	2.72727272727273	0.272727272727273
82	3	3.46043165467626	-0.460431654676259
83	4	3.46043165467626	0.539568345323741
84	2	3.46043165467626	-1.46043165467626
85	4	3.46043165467626	0.539568345323741
86	3	2.72727272727273	0.272727272727273
87	3	2.72727272727273	0.272727272727273
88	3	3.46043165467626	-0.460431654676259
89	2	3.46043165467626	-1.46043165467626
90	3	3.46043165467626	-0.460431654676259
91	2	2.72727272727273	-0.727272727272727
92	4	2.72727272727273	1.27272727272727
93	4	3.46043165467626	0.539568345323741
94	2	3.46043165467626	-1.46043165467626
95	1	2.72727272727273	-1.72727272727273
96	5	2.72727272727273	2.27272727272727
97	4	3.46043165467626	0.539568345323741
98	4	3.46043165467626	0.539568345323741
99	4	3.46043165467626	0.539568345323741
100	3	3.46043165467626	-0.460431654676259
101	3	3.46043165467626	-0.460431654676259
102	1	2.72727272727273	-1.72727272727273
103	5	3.46043165467626	1.53956834532374
104	3	3.46043165467626	-0.460431654676259
105	3	3.46043165467626	-0.460431654676259
106	2	2.72727272727273	-0.727272727272727
107	4	3.46043165467626	0.539568345323741
108	4	2.72727272727273	1.27272727272727
109	3	3.46043165467626	-0.460431654676259
110	4	2.72727272727273	1.27272727272727
111	4	3.46043165467626	0.539568345323741
112	2	2.72727272727273	-0.727272727272727
113	3	2.72727272727273	0.272727272727273
114	3	3.46043165467626	-0.460431654676259
115	3	3.46043165467626	-0.460431654676259
116	4	3.46043165467626	0.539568345323741
117	5	2.72727272727273	2.27272727272727
118	3	3.46043165467626	-0.460431654676259
119	3	3.46043165467626	-0.460431654676259
120	2	2.72727272727273	-0.727272727272727
121	3	3.46043165467626	-0.460431654676259
122	1	2.72727272727273	-1.72727272727273
123	4	3.46043165467626	0.539568345323741
124	4	3.46043165467626	0.539568345323741
125	4	2.72727272727273	1.27272727272727
126	3	3.46043165467626	-0.460431654676259
127	5	3.46043165467626	1.53956834532374
128	2	3.46043165467626	-1.46043165467626
129	2	3.46043165467626	-1.46043165467626
130	3	3.46043165467626	-0.460431654676259
131	3	2.72727272727273	0.272727272727273
132	2	3.46043165467626	-1.46043165467626
133	1	3.46043165467626	-2.46043165467626
134	3	2.72727272727273	0.272727272727273
135	5	3.46043165467626	1.53956834532374
136	4	3.46043165467626	0.539568345323741
137	4	3.46043165467626	0.539568345323741
138	4	3.46043165467626	0.539568345323741
139	3	3.46043165467626	-0.460431654676259
140	5	2.72727272727273	2.27272727272727
141	3	3.46043165467626	-0.460431654676259
142	3	2.72727272727273	0.272727272727273
143	3	2.72727272727273	0.272727272727273
144	3	3.46043165467626	-0.460431654676259
145	4	3.46043165467626	0.539568345323741
146	2	2.72727272727273	-0.727272727272727
147	2	2.72727272727273	-0.727272727272727
148	4	2.72727272727273	1.27272727272727
149	3	2.72727272727273	0.272727272727273
150	3	3.46043165467626	-0.460431654676259
151	2	2.72727272727273	-0.727272727272727
152	3	3.46043165467626	-0.460431654676259
153	3	3.46043165467626	-0.460431654676259
154	4	3.46043165467626	0.539568345323741
155	1	2.72727272727273	-1.72727272727273
156	1	3.46043165467626	-2.46043165467626
157	5	3.46043165467626	1.53956834532374
158	4	3.46043165467626	0.539568345323741
159	3	3.46043165467626	-0.460431654676259
160	3	3.46043165467626	-0.460431654676259
161	4	3.46043165467626	0.539568345323741
162	3	3.46043165467626	-0.460431654676259
163	2	2.72727272727273	-0.727272727272727
164	1	2.72727272727273	-1.72727272727273
165	1	2.72727272727273	-1.72727272727273
166	5	3.46043165467626	1.53956834532374
167	4	3.46043165467626	0.539568345323741
168	3	3.46043165467626	-0.460431654676259
169	4	3.46043165467626	0.539568345323741
170	5	3.46043165467626	1.53956834532374
171	4	3.46043165467626	0.539568345323741
172	4	2.72727272727273	1.27272727272727
173	2	3.46043165467626	-1.46043165467626
174	3	3.46043165467626	-0.460431654676259
175	4	3.46043165467626	0.539568345323741
176	3	3.46043165467626	-0.460431654676259
177	4	3.46043165467626	0.539568345323741
178	3	3.46043165467626	-0.460431654676259
179	4	3.46043165467626	0.539568345323741
180	1	2.72727272727273	-1.72727272727273
181	2	3.46043165467626	-1.46043165467626
182	3	3.46043165467626	-0.460431654676259
183	3	3.46043165467626	-0.460431654676259
184	5	3.46043165467626	1.53956834532374
185	4	3.46043165467626	0.539568345323741
186	3	3.46043165467626	-0.460431654676259
187	3	3.46043165467626	-0.460431654676259
188	3	2.72727272727273	0.272727272727273
189	3	3.46043165467626	-0.460431654676259
190	4	3.46043165467626	0.539568345323741
191	3	3.46043165467626	-0.460431654676259
192	2	2.72727272727273	-0.727272727272727
193	4	2.72727272727273	1.27272727272727
194	2	3.46043165467626	-1.46043165467626

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931980458le5utnezz9mmxf/2jhql1293198134.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931980458le5utnezz9mmxf/2jhql1293198134.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931980458le5utnezz9mmxf/3cqq61293198134.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931980458le5utnezz9mmxf/3cqq61293198134.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931980458le5utnezz9mmxf/44ip91293198134.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12931980458le5utnezz9mmxf/44ip91293198134.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

R code (references can be found in the software module):

library(party)

library(Hmisc)

par1 <- as.numeric(par1)

par3 <- as.numeric(par3)

x <- data.frame(t(y))

is.data.frame(x)

x <- x[!is.na(x[,par1]),]

k <- length(x[1,])

n <- length(x[,1])

colnames(x)[par1]

x[,par1]

if (par2 == 'kmeans') {

cl <- kmeans(x[,par1], par3)

print(cl)

clm <- matrix(cbind(cl$centers,1:par3),ncol=2)

clm <- clm[sort.list(clm[,1]),]

for (i in 1:par3) {

cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')

}

cl$cluster <- as.factor(cl$cluster)

print(cl$cluster)

x[,par1] <- cl$cluster

}

if (par2 == 'quantiles') {

x[,par1] <- cut2(x[,par1],g=par3)

}

if (par2 == 'hclust') {

hc <- hclust(dist(x[,par1])^2, 'cen')

print(hc)

memb <- cutree(hc, k = par3)

dum <- c(mean(x[memb==1,par1]))

for (i in 2:par3) {

dum <- c(dum, mean(x[memb==i,par1]))

}

hcm <- matrix(cbind(dum,1:par3),ncol=2)

hcm <- hcm[sort.list(hcm[,1]),]

for (i in 1:par3) {

memb[memb==hcm[i,2]] <- paste('C',i,sep='')

}

memb <- as.factor(memb)

print(memb)

x[,par1] <- memb

}

if (par2=='equal') {

ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))

x[,par1] <- as.factor(ed)

}

table(x[,par1])

colnames(x)

colnames(x)[par1]

x[,par1]

if (par2 == 'none') {

m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)

}

load(file='createtable')

if (par2 != 'none') {

m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)

if (par4=='yes') {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'',1,TRUE)

a<-table.element(a,'Prediction (training)',par3+1,TRUE)

a<-table.element(a,'Prediction (testing)',par3+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Actual',1,TRUE)

for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)

a<-table.element(a,'CV',1,TRUE)

for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)

a<-table.element(a,'CV',1,TRUE)

a<-table.row.end(a)

for (i in 1:10) {

ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))

m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])

if (i==1) {

m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])

m.ct.i.actu <- x[ind==1,par1]

m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])

m.ct.x.actu <- x[ind==2,par1]

} else {

m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))

m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])

m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))

m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])

}

}

print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))

numer <- 0

for (i in 1:par3) {

print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))

numer <- numer + m.ct.i.tab[i,i]

}

print(m.ct.i.cp <- numer / sum(m.ct.i.tab))

print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))

numer <- 0

for (i in 1:par3) {

print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))

numer <- numer + m.ct.x.tab[i,i]

}

print(m.ct.x.cp <- numer / sum(m.ct.x.tab))

for (i in 1:par3) {

a<-table.row.start(a)

a<-table.element(a,paste('C',i,sep=''),1,TRUE)

for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])

a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))

for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])

a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))

a<-table.row.end(a)

}

a<-table.row.start(a)

a<-table.element(a,'Overall',1,TRUE)

for (jjj in 1:par3) a<-table.element(a,'-')

a<-table.element(a,round(m.ct.i.cp,4))

for (jjj in 1:par3) a<-table.element(a,'-')

a<-table.element(a,round(m.ct.x.cp,4))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

}

}

m

bitmap(file='test1.png')

plot(m)

dev.off()

bitmap(file='test1a.png')

plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')

dev.off()

if (par2 == 'none') {

forec <- predict(m)

result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))

colnames(result) <- c('Actuals','Forecasts','Residuals')

print(result)

}

if (par2 != 'none') {

print(cbind(as.factor(x[,par1]),predict(m)))

myt <- table(as.factor(x[,par1]),predict(m))

print(myt)

}

bitmap(file='test2.png')

if(par2=='none') {

op <- par(mfrow=c(2,2))

plot(density(result$Actuals),main='Kernel Density Plot of Actuals')

plot(density(result$Residuals),main='Kernel Density Plot of Residuals')

plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')

plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')

par(op)

}

if(par2!='none') {

plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')

}

dev.off()

if (par2 == 'none') {

detcoef <- cor(result$Forecasts,result$Actuals)

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goodness of Fit',2,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Correlation',1,TRUE)

a<-table.element(a,round(detcoef,4))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'R-squared',1,TRUE)

a<-table.element(a,round(detcoef*detcoef,4))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'RMSE',1,TRUE)

a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'#',header=TRUE)

a<-table.element(a,'Actuals',header=TRUE)

a<-table.element(a,'Forecasts',header=TRUE)

a<-table.element(a,'Residuals',header=TRUE)

a<-table.row.end(a)

for (i in 1:length(result$Actuals)) {

a<-table.row.start(a)

a<-table.element(a,i,header=TRUE)

a<-table.element(a,result$Actuals[i])

a<-table.element(a,result$Forecasts[i])

a<-table.element(a,result$Residuals[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable.tab')

}

if (par2 != 'none') {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'',1,TRUE)

for (i in 1:par3) {

a<-table.element(a,paste('C',i,sep=''),1,TRUE)

}

a<-table.row.end(a)

for (i in 1:par3) {

a<-table.row.start(a)

a<-table.element(a,paste('C',i,sep=''),1,TRUE)

for (j in 1:par3) {

a<-table.element(a,myt[i,j])

}

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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